Dynamic programming-based hot spot identification approach for pedestrian crashes. (August 2016)

• Record Type: Journal Article
• Authors: Medury, Aditya; Grembek, Offer
• Is Part Of: Accident analysis and prevention. Volume 93(2016)
• Page Start: 198
• Page End: 206
• Publication Date: 2016-08
• Subjects: Hot spot analysis -- Sliding window -- Dynamic programming -- Pedestrian safety -- Network screening; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2016.04.037
• Languages: English
• ISSNs: 0001-4575

Abstract

Abstract : Highlights: Current network screening methods do not employ network constraints/objectives. Dynamic programming can incorporate these considerations in an optimal manner. Proposed method covers more crashes for a range of hot spot selection thresholds. Merits/demerits of methods discussed in the context of pedestrian hot spot detection. Abstract: Network screening techniques are widely used by state agencies to identify locations with high collision concentration, also referred to as hot spots. However, most of the research in this regard has focused on identifying highway segments that are of concern to automobile collisions. In comparison, pedestrian hot spot detection has typically focused on analyzing pedestrian crashes in specific locations, such as at/near intersections, mid-blocks, and/or other crossings, as opposed to long stretches of roadway. In this context, the efficiency of the some of the widely used network screening methods has not been tested. Hence, in order to address this issue, a dynamic programming-based hot spot identification approach is proposed which provides efficient hot spot definitions for pedestrian crashes. The proposed approach is compared with the sliding window method and an intersection buffer-based approach. The results reveal that the dynamic programming method generates more hot spots with a higher number of crashes, while providing small hot spot segment lengths. In comparison, the sliding window method is shown to suffer from shortcomings due to a first-come-first-serve approach vis-à-vis hot spot identification and a fixed hot spot window length assumption.